On More General Distributions of Random Binning for Slepian-Wolf Encoding
Neri Merhav

TL;DR
This paper explores generalized random binning distributions for Slepian-Wolf encoding, analyzing their impact on error and source coding exponents, and demonstrates that broader ensembles can outperform traditional variable-rate schemes in certain scenarios.
Contribution
It introduces a wider class of random binning distributions for Slepian-Wolf coding, showing they can outperform traditional schemes and offer robustness against system failures.
Findings
Wider ensembles match VRSW in error and source coding trade-offs.
Wider ensembles outperform VRSW in pathological cases.
Proposed ensembles enable robust reconstruction despite system failures.
Abstract
Traditionally, ensembles of Slepian-Wolf (SW) codes are defined such that every bin of each -vector of each source is randomly drawn under the uniform distribution across the sets and , where and are the coding rates of the two sources, and , respectively. In a few more recent works, where only one source, say, , is compressed and the other one, , serves as side information available at the decoder, the scope is extended to variable-rate S-W (VRSW) codes, where the rate is allowed to depend on the type class of the source string, but still, the random-binning distribution is assumed uniform within the corresponding, type-dependent, bin index set. In this expository work, we investigate the role of the uniformity of the random binning distribution from the perspective of the trade-off between the…
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